Longitudinal Work as long as ce Analysis using Routinely Collected Data: Challenges in addition to Possibilities Longitudinal Analysis Work as long as ce Data Example: NMDS-SC NMDS-SC data structure NMDS-SC longitudinal analysis: potential

Longitudinal Work as long as ce Analysis using Routinely Collected Data: Challenges in addition to Possibilities Longitudinal Analysis Work as long as ce Data Example: NMDS-SC NMDS-SC data structure NMDS-SC longitudinal analysis: potential www.phwiki.com

Longitudinal Work as long as ce Analysis using Routinely Collected Data: Challenges in addition to Possibilities Longitudinal Analysis Work as long as ce Data Example: NMDS-SC NMDS-SC data structure NMDS-SC longitudinal analysis: potential

Sialega, Salina, Managing Editor has reference to this Academic Journal, PHwiki organized this Journal Longitudinal Work as long as ce Analysis using Routinely Collected Data: Challenges in addition to Possibilities Shereen Hussein, BSc MSc PhD King’s College London Longitudinal Analysis General advantages Can control as long as individual heterogeneity Subject serve as own control Between-subject variation excluded from error Can better assess causality than cross-sectional data General challenges Conventional statistical methods require independence between observations Longitudinal data are likely to violate this assumption Missing data due to attrition Data availability 29/5/2012 Work as long as ce Data Example: NMDS-SC Structure Design Coverage Time span Type of in as long as mation collected Data collection in addition to archiving size 29/5/2012

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NMDS-SC data structure Social care providers in Engl in addition to Complete NMDS-SC returns Aggregate in as long as mation on the work as long as ce Detailed in as long as mation on all or some individual workers Providers’ Database workers’ Database Linkable 29/5/2012 NMDS-SC longitudinal analysis: potential Data coverage Wide range of providers in addition to individual workers’ in as long as mation Sector specific- uniqueness Hierarchical structure Work as long as ce development in addition to business sustainability Timely Demographics, austerity, unemployment Economics Care costs, including turnover costs Pay Linkable to local data characteristics 29/5/2012 Challenges in NMDS-SC longitudinal analysis No sampling framework No regular intervals as long as data collection Irregularities in data completion by different providers Additions/alterations of variables in addition to fields Cumulative nature in addition to consequences on data size in addition to structure Archiving 29/5/2012

Challenges in NMDS-SC longitudinal analysis- continued Computational Data size Innovation in system design in addition to architecture Accumulative property Scalability of the system Changes in data fields Variable additions in addition to omissions Data over-ride in addition to archiving Software in addition to hardware issues Methodological Unusual patterns of follow-up Censoring Variability in the database over time Unbalanced cohort design Missing data Update frequency Attrition True exit Other methodological issues 29/5/2012 Providers’ level longitudinal mapping From December 2007 to March 2011 Linked 18 separate databases on the providers’ level Each has records from 13,095 to 25,266 421,671 valid records included in the construction Number of updates ranged from 0 to 18 per provider Continuous process, more records added every 3 months 29/5/2012 Meta-data analysis: providers with different number of events 29/5/2012

Specific example 1: Providers with 18 updates 29/5/2012 29/5/2012 Specific example 2: Providers with 2 updates Density distribution plot of providers with at least 2 updates during the period December 2007 to March 2011 29/5/2012

density distributions of number of days elapsed between two updated providers’ events 29/5/2012 Simple example using providers’ database: work as long as ce stability over time Longitudinal changes in care workers’ turnover in addition to vacancy rates over time From January 2008 to January 2010 Changes in reasons as long as leaving the sector, identified by employers Differentiating between those with improved (reduced) turnover rates in addition to those with worse (increased) turnover rates 29/5/2012 Pre analysis Selecting in addition to constructing providers’ panel Including those with at least two updates within +/- 3 months of T1 in addition to T2 2953 providers with mean coverage duration of 602d Investigate sample representation Data quality checks Data manipulation/imputation 29/5/2012

Some findings: changes in turnover rates 29/5/2012 Reason as long as leaving in addition to turnover rate changes 29/5/2012 Analysis expansion: next steps Consider changes over a longer period of time Examine other providers’ characteristics Different take on panel inclusion criteria Link to individual workers’ longitudinal databases to examine relations with detailed work as long as ce structure Pay, qualifications, profile etc. Build economic elements within analyses models, e.g. specific-turnover costs, within the longitudinal model 29/5/2012

Workers’ level longitudinal analysis A much larger database Same period of time- over 11M records Providers not required to complete in as long as mation as long as ‘all’ workers Structural/design missing data True missing data Linkage issues more data fields required as long as identification in addition to linkage Considerably large number of variables in addition to fields Careful planning; analysis-tailored data retrieval Changes in database Amendments, new variables etc. Programming intensive in addition to dem in addition to ing models (may not be replicable as long as different databases) 29/5/2012 29/5/2012 Issues to consider Suitability of models Longitudinal structure Competing risks Measurement window Late entry into risk sets Use proxies, other variables in the dataset Adopt suitable approach/model Censoring (LHS in addition to RHS) Assumptions Guided by: Sector-specific knowledge Intelligence from other variables in the data 29/5/2012

Current longitudinal research Watch this space!! Work as long as ce mobility within the sector Occupation durations Characteristic-specific probabilities of exiting or remaining in the sector Characteristic-specific probabilities of moving employer within the sector or having multiple jobs Career pathways within the sector 29/5/2012 Acknowledgments Thanks to the Department of Health as long as funding this work Thanks to Skills as long as Care as long as providing the data on regular basis Thanks to Analytical Research Ltd as long as their technical in addition to quantitative support 29/5/2012 Further in as long as mation Shereen.hussein@kcl.ac.uk 02078481669 See: http://www.kcl.ac.uk/sspp/departments/sshm/scwru/res/knowledge/nmdslong.aspx 29/5/2012

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